Overview

Dataset statistics

Number of variables7
Number of observations647
Missing cells191
Missing cells (%)4.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.4 KiB
Average record size in memory59.2 B

Variable types

Numeric3
Categorical1
Text2
DateTime1

Dataset

Description대구광역시 북구_지하수시설_20190828
Author대구광역시 북구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15006310&dataSetDetailId=150063102fd7ed84593fc_201908291457&provdMethod=FILE

Alerts

데이터기준일자 has constant value ""Constant
지하수개발도로명주소 has 191 (29.5%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-04-22 00:27:54.491538
Analysis finished2024-04-22 00:27:55.684108
Duration1.19 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct647
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean324
Minimum1
Maximum647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-04-22T09:27:55.754687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33.3
Q1162.5
median324
Q3485.5
95-th percentile614.7
Maximum647
Range646
Interquartile range (IQR)323

Descriptive statistics

Standard deviation186.91709
Coefficient of variation (CV)0.57690461
Kurtosis-1.2
Mean324
Median Absolute Deviation (MAD)162
Skewness0
Sum209628
Variance34938
MonotonicityStrictly increasing
2024-04-22T09:27:55.898459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
446 1
 
0.2%
428 1
 
0.2%
429 1
 
0.2%
430 1
 
0.2%
431 1
 
0.2%
432 1
 
0.2%
433 1
 
0.2%
434 1
 
0.2%
435 1
 
0.2%
Other values (637) 637
98.5%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
647 1
0.2%
646 1
0.2%
645 1
0.2%
644 1
0.2%
643 1
0.2%
642 1
0.2%
641 1
0.2%
640 1
0.2%
639 1
0.2%
638 1
0.2%

용도
Categorical

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
생활용
440 
농·어업용
165 
공업용
 
42

Length

Max length5
Median length3
Mean length3.5100464
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row생활용
2nd row생활용
3rd row생활용
4th row생활용
5th row생활용

Common Values

ValueCountFrequency (%)
생활용 440
68.0%
농·어업용 165
 
25.5%
공업용 42
 
6.5%

Length

2024-04-22T09:27:56.039783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T09:27:56.140050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활용 440
68.0%
농·어업용 165
 
25.5%
공업용 42
 
6.5%

지하수개발지번주소
Text

Distinct632
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
2024-04-22T09:27:56.439878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length27
Mean length20.758887
Min length17

Characters and Unicode

Total characters13431
Distinct characters67
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique619 ?
Unique (%)95.7%

Sample

1st row대구광역시 북구 도남동 720번지 2호
2nd row대구광역시 북구 도남동 605번지
3rd row대구광역시 북구 도남동 511번지
4th row대구광역시 북구 도남동 471번지
5th row대구광역시 북구 도남동 604번지
ValueCountFrequency (%)
대구광역시 647
21.5%
북구 647
21.5%
1호 124
 
4.1%
칠성동1가 77
 
2.6%
2호 66
 
2.2%
서변동 50
 
1.7%
산격동 48
 
1.6%
국우동 45
 
1.5%
3호 43
 
1.4%
도남동 41
 
1.4%
Other values (525) 1228
40.7%
2024-04-22T09:27:56.907271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2574
19.2%
구 1323
 
9.9%
동 683
 
5.1%
대 657
 
4.9%
광 647
 
4.8%
역 647
 
4.8%
시 647
 
4.8%
북 647
 
4.8%
번 647
 
4.8%
지 647
 
4.8%
Other values (57) 4312
32.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8338
62.1%
Space Separator 2574
 
19.2%
Decimal Number 2477
 
18.4%
Close Punctuation 16
 
0.1%
Open Punctuation 16
 
0.1%
Lowercase Letter 5
 
< 0.1%
Dash Punctuation 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 1323
15.9%
동 683
8.2%
대 657
7.9%
광 647
7.8%
역 647
7.8%
시 647
7.8%
북 647
7.8%
번 647
7.8%
지 647
7.8%
호 439
 
5.3%
Other values (42) 1354
16.2%
Decimal Number
ValueCountFrequency (%)
1 535
21.6%
2 311
12.6%
3 268
10.8%
5 247
10.0%
4 237
9.6%
7 222
9.0%
6 182
 
7.3%
0 164
 
6.6%
8 162
 
6.5%
9 149
 
6.0%
Space Separator
ValueCountFrequency (%)
2574
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Lowercase Letter
ValueCountFrequency (%)
w 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8338
62.1%
Common 5088
37.9%
Latin 5
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 1323
15.9%
동 683
8.2%
대 657
7.9%
광 647
7.8%
역 647
7.8%
시 647
7.8%
북 647
7.8%
번 647
7.8%
지 647
7.8%
호 439
 
5.3%
Other values (42) 1354
16.2%
Common
ValueCountFrequency (%)
2574
50.6%
1 535
 
10.5%
2 311
 
6.1%
3 268
 
5.3%
5 247
 
4.9%
4 237
 
4.7%
7 222
 
4.4%
6 182
 
3.6%
0 164
 
3.2%
8 162
 
3.2%
Other values (4) 186
 
3.7%
Latin
ValueCountFrequency (%)
w 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8338
62.1%
ASCII 5093
37.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2574
50.5%
1 535
 
10.5%
2 311
 
6.1%
3 268
 
5.3%
5 247
 
4.8%
4 237
 
4.7%
7 222
 
4.4%
6 182
 
3.6%
0 164
 
3.2%
8 162
 
3.2%
Other values (5) 191
 
3.8%
Hangul
ValueCountFrequency (%)
구 1323
15.9%
동 683
8.2%
대 657
7.9%
광 647
7.8%
역 647
7.8%
시 647
7.8%
북 647
7.8%
번 647
7.8%
지 647
7.8%
호 439
 
5.3%
Other values (42) 1354
16.2%

지하수개발도로명주소
Text

MISSING 

Distinct433
Distinct (%)95.0%
Missing191
Missing (%)29.5%
Memory size5.2 KiB
2024-04-22T09:27:57.169499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length29
Mean length24.563596
Min length19

Characters and Unicode

Total characters11201
Distinct characters109
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique414 ?
Unique (%)90.8%

Sample

1st row대구광역시 북구 도남길 138 (도남동)
2nd row대구광역시 북구 도남길 148-57 (도남동)
3rd row대구광역시 북구 도남길 148-35 (도남동)
4th row대구광역시 북구 호국로 299 (서변동)
5th row대구광역시 북구 도남길 219-4 (도남동)
ValueCountFrequency (%)
북구 456
20.0%
대구광역시 452
19.8%
칠성동1가 71
 
3.1%
산격동 43
 
1.9%
칠성시장로3길 41
 
1.8%
도남길 33
 
1.4%
침산동 30
 
1.3%
노원동3가 30
 
1.3%
태전동 27
 
1.2%
도남동 26
 
1.1%
Other values (509) 1072
47.0%
2024-04-22T09:27:57.538829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1831
16.3%
구 962
 
8.6%
대 531
 
4.7%
시 524
 
4.7%
동 517
 
4.6%
북 477
 
4.3%
) 467
 
4.2%
( 466
 
4.2%
광 456
 
4.1%
역 456
 
4.1%
Other values (99) 4514
40.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6594
58.9%
Space Separator 1831
 
16.3%
Decimal Number 1685
 
15.0%
Close Punctuation 467
 
4.2%
Open Punctuation 466
 
4.2%
Dash Punctuation 156
 
1.4%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 962
14.6%
대 531
 
8.1%
시 524
 
7.9%
동 517
 
7.8%
북 477
 
7.2%
광 456
 
6.9%
역 456
 
6.9%
로 381
 
5.8%
길 254
 
3.9%
칠 193
 
2.9%
Other values (84) 1843
27.9%
Decimal Number
ValueCountFrequency (%)
1 421
25.0%
2 240
14.2%
3 231
13.7%
4 137
 
8.1%
5 134
 
8.0%
8 120
 
7.1%
6 120
 
7.1%
0 96
 
5.7%
7 95
 
5.6%
9 91
 
5.4%
Space Separator
ValueCountFrequency (%)
1831
100.0%
Close Punctuation
ValueCountFrequency (%)
) 467
100.0%
Open Punctuation
ValueCountFrequency (%)
( 466
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 156
100.0%
Lowercase Letter
ValueCountFrequency (%)
w 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6594
58.9%
Common 4605
41.1%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 962
14.6%
대 531
 
8.1%
시 524
 
7.9%
동 517
 
7.8%
북 477
 
7.2%
광 456
 
6.9%
역 456
 
6.9%
로 381
 
5.8%
길 254
 
3.9%
칠 193
 
2.9%
Other values (84) 1843
27.9%
Common
ValueCountFrequency (%)
1831
39.8%
) 467
 
10.1%
( 466
 
10.1%
1 421
 
9.1%
2 240
 
5.2%
3 231
 
5.0%
- 156
 
3.4%
4 137
 
3.0%
5 134
 
2.9%
8 120
 
2.6%
Other values (4) 402
 
8.7%
Latin
ValueCountFrequency (%)
w 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6594
58.9%
ASCII 4607
41.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1831
39.7%
) 467
 
10.1%
( 466
 
10.1%
1 421
 
9.1%
2 240
 
5.2%
3 231
 
5.0%
- 156
 
3.4%
4 137
 
3.0%
5 134
 
2.9%
8 120
 
2.6%
Other values (5) 404
 
8.8%
Hangul
ValueCountFrequency (%)
구 962
14.6%
대 531
 
8.1%
시 524
 
7.9%
동 517
 
7.8%
북 477
 
7.2%
광 456
 
6.9%
역 456
 
6.9%
로 381
 
5.8%
길 254
 
3.9%
칠 193
 
2.9%
Other values (84) 1843
27.9%

위도
Real number (ℝ)

Distinct610
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.917254
Minimum35.874154
Maximum35.974726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-04-22T09:27:57.683199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.874154
5-th percentile35.874809
Q135.892973
median35.915507
Q335.942456
95-th percentile35.962347
Maximum35.974726
Range0.10057213
Interquartile range (IQR)0.049482335

Descriptive statistics

Standard deviation0.029005386
Coefficient of variation (CV)0.00080756133
Kurtosis-1.2383044
Mean35.917254
Median Absolute Deviation (MAD)0.02438647
Skewness0.09943938
Sum23238.464
Variance0.0008413124
MonotonicityNot monotonic
2024-04-22T09:27:57.819072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.89594143 5
 
0.8%
35.93970738 4
 
0.6%
35.87453704 3
 
0.5%
35.87462466 3
 
0.5%
35.94303608 2
 
0.3%
35.96239947 2
 
0.3%
35.95371572 2
 
0.3%
35.8921035 2
 
0.3%
35.89948186 2
 
0.3%
35.94148712 2
 
0.3%
Other values (600) 620
95.8%
ValueCountFrequency (%)
35.87415403 1
0.2%
35.87425301 1
0.2%
35.87432505 1
0.2%
35.87434464 1
0.2%
35.87437509 1
0.2%
35.87438215 1
0.2%
35.87441758 1
0.2%
35.87442855 2
0.3%
35.87442944 1
0.2%
35.87444618 1
0.2%
ValueCountFrequency (%)
35.97472616 1
0.2%
35.97441181 1
0.2%
35.97249116 1
0.2%
35.97215003 1
0.2%
35.97194319 1
0.2%
35.9711722 1
0.2%
35.97098692 1
0.2%
35.97081017 1
0.2%
35.97026762 1
0.2%
35.96997417 1
0.2%

경도
Real number (ℝ)

Distinct610
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.58302
Minimum128.50776
Maximum128.63009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-04-22T09:27:57.955852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.50776
5-th percentile128.53672
Q1128.56301
median128.58798
Q3128.60409
95-th percentile128.61897
Maximum128.63009
Range0.1223267
Interquartile range (IQR)0.0410831

Descriptive statistics

Standard deviation0.026924061
Coefficient of variation (CV)0.00020939048
Kurtosis-0.72766921
Mean128.58302
Median Absolute Deviation (MAD)0.0181062
Skewness-0.52959284
Sum83193.216
Variance0.00072490505
MonotonicityNot monotonic
2024-04-22T09:27:58.098999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.580274 5
 
0.8%
128.5637035 4
 
0.6%
128.603595 3
 
0.5%
128.604094 3
 
0.5%
128.5893197 2
 
0.3%
128.5448738 2
 
0.3%
128.580276 2
 
0.3%
128.6054344 2
 
0.3%
128.609667 2
 
0.3%
128.5666422 2
 
0.3%
Other values (600) 620
95.8%
ValueCountFrequency (%)
128.5077635 1
0.2%
128.5106262 1
0.2%
128.5167786 1
0.2%
128.5182961 1
0.2%
128.5197308 1
0.2%
128.5203229 1
0.2%
128.524901 1
0.2%
128.5252897 1
0.2%
128.5254788 1
0.2%
128.5255801 1
0.2%
ValueCountFrequency (%)
128.6300902 1
0.2%
128.6297768 2
0.3%
128.6293447 1
0.2%
128.6274602 1
0.2%
128.6273987 1
0.2%
128.6264166 1
0.2%
128.6261176 1
0.2%
128.6256391 1
0.2%
128.6253366 1
0.2%
128.6252902 1
0.2%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
Minimum2019-08-28 00:00:00
Maximum2019-08-28 00:00:00
2024-04-22T09:27:58.213563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:27:58.319385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-22T09:27:55.237715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:27:54.737900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:27:54.986173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:27:55.323348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:27:54.823285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:27:55.071623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:27:55.405261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:27:54.905542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:27:55.150406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-22T09:27:58.396138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번용도위도경도
연번1.0000.3840.5610.509
용도0.3841.0000.4930.343
위도0.5610.4931.0000.729
경도0.5090.3430.7291.000
2024-04-22T09:27:58.751653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도용도
연번1.0000.0930.0370.249
위도0.0931.000-0.2350.339
경도0.037-0.2351.0000.218
용도0.2490.3390.2181.000

Missing values

2024-04-22T09:27:55.522327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-22T09:27:55.639931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

연번용도지하수개발지번주소지하수개발도로명주소위도경도데이터기준일자
01생활용대구광역시 북구 도남동 720번지 2호대구광역시 북구 도남길 138 (도남동)35.960621128.5837082019-08-28
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